Nanotechnology has provided a fourth fundamental two-terminal passive element for electronic circuits—one first theoretically predicted 37 years ago, but only made physically possible by nanotechnology. Now joining resistors, capacitors and inductors is a nanotech device named the ‘memristor’, developed by a team led by R. Stanley Williams, who shared the 2000 Foresight Nanotech Institute Feynman Prize for Experimental work. From “H.P. Unveils New Memory Technology“, written by John Markoff for The New York TImes:
A team of Hewlett-Packard scientists reported Wednesday in the science journal Nature [abstract] that they have designed a simple circuit element they believe will enable tiny powerful computers that could imitate biological functions.
The device, called a memristor, could make it possible to build extremely dense computer memory chips that use far less power than today’s DRAM memory chips, which are rapidly reaching the limit in how much smaller they can be made.
The memristor, an electrical resistor with memory properties, may also make it possible to fashion advanced logic circuits, like a class of reprogrammable chips known as field programmable gate arrays, that are today widely used for rapid prototyping of new circuits and for custom-made chips that need to be manufactured quickly.
Potentially even more tantalizing is the memristors’ ability to store and retrieve a vast array of intermediate values, not just the binary 1s and 0s as conventional chips do. This makes them function like biological synapses, which would be ideal for many artificial intelligence applications ranging from machine vision to understanding speech.
The H.P. researchers said that the discovery of the memory properties in tiny, extremely thin spots of titanium dioxide, came from a frustrating, decade-long hunt for a new class of organic molecules to serve as nano-sized switches. Researchers in both industry and academia have hoped they would be able to fashion switches as small as the size of a single molecule to someday replace transistors once the semiconductor industry’s shrinking of electronic circuits made with photolithographic techniques reached a technological limit.
…The material offers a new approach that is radically different than another type of solid state storage called “phase-change memory” that is now being pursued by I.B.M., Intel and other companies. In a phase-change memory heat is used to shift a glassy material from an amorphous to a crystalline state and back again. The switching speed of these systems is both slower and requires more power, according to the H.P. scientists.
The memristor technology should be fairly quickly commercialized, said R. Stanley Williams, director of the quantum science research group at H.P. “This is on a fast track,” he said.
The memristor was predicted in 1971 by a Berkeley electrical engineer, Leon Chua. There have been hints of an unexplained behavior in the literature for some time, Mr. Chua said in a phone interview on Tuesday.
However, he noted that he had not worked on his idea for several decades and that he was taken by surprise when he was contacted by the H.P. researchers several months ago. The advance clearly points the way to a prediction made in 1959 by the physicist, Richard Feynman, that “there’s plenty of room at the bottom,” referring to the possibility of building atomic-scale systems.
“I can see all kinds of new technologies and I’m thrilled,” he said.
As James M. Tour and Tao He write in a News & Views editorial that accompanies the research article, the name of the new device is a contraction of ‘memory resistor’ because the device shows a hysteresis effect in which the resistance of the device depends on the previous voltage across the device. This property of ‘memristance’ is minute in microscale chips, but becomes substantial in the nanoscale. Besides explaining a number of puzzling phenomena in nanoelectronics, “The memristor might provide a new path onwards and downwards to ever-greater processor density,” they write. Williams and his co-workers state in concluding their research article that “Important applications include ultradense, semi-non-volatile memories and learning networks that require a synapse-like function.”